NJUNLP’s Participation for the WMT2022 Quality Estimation Shared Task
Xiang Geng, Yu Zhang, Shujian Huang, Shimin Tao, Hao Yang, Jiajun Chen
Abstract
This paper presents submissions of the NJUNLP team in WMT 2022Quality Estimation shared task 1, where the goal is to predict the sentence-level and word-level quality for target machine translations. Our system explores pseudo data and multi-task learning. We propose several novel methods to generate pseudo data for different annotations using the conditional masked language model and the neural machine translation model. The proposed methods control the decoding process to generate more real pseudo translations. We pre-train the XLMR-large model with pseudo data and then fine-tune this model with real data both in the way of multi-task learning. We jointly learn sentence-level scores (with regression and rank tasks) and word-level tags (with a sequence tagging task). Our system obtains competitive results on different language pairs and ranks first place on both sentence- and word-level sub-tasks of the English-German language pair.- Anthology ID:
- 2022.wmt-1.57
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 615–620
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.57
- DOI:
- Bibkey:
- Cite (ACL):
- Xiang Geng, Yu Zhang, Shujian Huang, Shimin Tao, Hao Yang, and Jiajun Chen. 2022. NJUNLP’s Participation for the WMT2022 Quality Estimation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 615–620, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- NJUNLP’s Participation for the WMT2022 Quality Estimation Shared Task (Geng et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.57.pdf
Export citation
@inproceedings{geng-etal-2022-njunlps,
title = "{NJUNLP}{'}s Participation for the {WMT}2022 Quality Estimation Shared Task",
author = "Geng, Xiang and
Zhang, Yu and
Huang, Shujian and
Tao, Shimin and
Yang, Hao and
Chen, Jiajun",
editor = {Koehn, Philipp and
Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.57",
pages = "615--620",
abstract = "This paper presents submissions of the NJUNLP team in WMT 2022Quality Estimation shared task 1, where the goal is to predict the sentence-level and word-level quality for target machine translations. Our system explores pseudo data and multi-task learning. We propose several novel methods to generate pseudo data for different annotations using the conditional masked language model and the neural machine translation model. The proposed methods control the decoding process to generate more real pseudo translations. We pre-train the XLMR-large model with pseudo data and then fine-tune this model with real data both in the way of multi-task learning. We jointly learn sentence-level scores (with regression and rank tasks) and word-level tags (with a sequence tagging task). Our system obtains competitive results on different language pairs and ranks first place on both sentence- and word-level sub-tasks of the English-German language pair.",
}
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%0 Conference Proceedings %T NJUNLP’s Participation for the WMT2022 Quality Estimation Shared Task %A Geng, Xiang %A Zhang, Yu %A Huang, Shujian %A Tao, Shimin %A Yang, Hao %A Chen, Jiajun %Y Koehn, Philipp %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F geng-etal-2022-njunlps %X This paper presents submissions of the NJUNLP team in WMT 2022Quality Estimation shared task 1, where the goal is to predict the sentence-level and word-level quality for target machine translations. Our system explores pseudo data and multi-task learning. We propose several novel methods to generate pseudo data for different annotations using the conditional masked language model and the neural machine translation model. The proposed methods control the decoding process to generate more real pseudo translations. We pre-train the XLMR-large model with pseudo data and then fine-tune this model with real data both in the way of multi-task learning. We jointly learn sentence-level scores (with regression and rank tasks) and word-level tags (with a sequence tagging task). Our system obtains competitive results on different language pairs and ranks first place on both sentence- and word-level sub-tasks of the English-German language pair. %U https://aclanthology.org/2022.wmt-1.57 %P 615-620
Markdown (Informal)
[NJUNLP’s Participation for the WMT2022 Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.57) (Geng et al., WMT 2022)
- NJUNLP’s Participation for the WMT2022 Quality Estimation Shared Task (Geng et al., WMT 2022)
ACL
- Xiang Geng, Yu Zhang, Shujian Huang, Shimin Tao, Hao Yang, and Jiajun Chen. 2022. NJUNLP’s Participation for the WMT2022 Quality Estimation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 615–620, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.